AI Can Code… But Can It Care? Exploring Automation in Qualitative Research
Bibliographic record
Abstract
Mounting demands for efficiency and productivity in research have created pressures for applied anthropologists to integrate artificial intelligence (AI) into their methodological toolkits. This reflexive essay considers the ethical implications of using AI technologies to code qualitative health data. Drawing on my experience as a medical anthropologist working in a psychiatric epidemiology consortium that shifted from human-driven to AI-driven coding, I suggest that AI cannot attune to the affective, moral, and situated dimensions that bring care to anthropological inquiry. Drawing on a feminist ethic of care, I examine how automation reconfigures economies, ecologies, epistemologies, and relationalities of care in the process of coding. Yet, despite its limitations and harms, an outright rejection of AI forecloses opportunities to imagine new and transformative relationships with this technology. I conclude that care is more than an ethical stance, it is a methodological praxis that requires renewed nurturing as anthropologists working in diverse field sites contend with trends in automation. While anthropologists have studied AI, algorithms, and automation as topical matter, there has not yet been sufficient attention to how AI itself becomes integrated into our own research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.375 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.093 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".